Project abstract The advanced signal-processing algorithms used in digital hearing aids have improved average hearing aid benefit and satisfaction. However, response to hearing aids is highly variable, with some individuals reporting much more benefit than others. This variability is most evident among older listeners. An important issue is what levels of advanced hearing aid processing are necessary to achieve success with hearing aids in individual listeners. Every form of nonlinear signal processing has its own set of trade-offs of improved audibility versus increased modification of the signal caused by the signal processing. However, there are no effective procedures for determining in clinical fittings who will benefit from the processing or how the processing should be adjusted for the individual listener. The long term goal of this research is use evidence- based clinical tests to guide the selection of signal processing that is most appropriate for individual older adults wearing hearing aids in their own listening environments. The proposed renewal application moves this work toward clinical application in three steps. The first specific aim is to characterize variability in response to signal manipulation among older adults and determine what patient factors are related to that variability under clinical conditions, and to use those data to modify our fitting metrics for more accurate application to clinical fittings. Data will be collected for hearing aids fit by audiologists using current best practice (i.e., standard of care) in our site clinics. Signal manipulation will be quantified and related to measures of aided intelligibility, quality, and preference. This dataset will allow us to extend our model-based approach to include a full continuum of signal processing for patients with a wide range of hearing loss configurations. In the second specific aim, the clinical toolset will be implemented in a computer-based application that can guide audiologists in the fitting and adjustment of signal processing based on individual listener characteristics. Application partners will work with us to develop the necessary software and hardware (a low-noise probe microphone system) capable of measuring hearing aid output and inputting those values to the computer application. The third specific aim is to validate clinical use of the toolset by comparing a population of patients fit with the toolset to those fit using current standard of care. Data will be collected on patient outcomes, clinical impact (number of visits needed to adjust the hearing aids) and audiologist feedback regarding professional confidence. Clinician feedback will also be collected to refine implementation of the clinical application and improve its usability. It is hypothesized that hearing aid fittings completed using the clinical toolset will result in better patient outcomes, fewer post-fitting visits and a higher level of clinician confidence compared to standard-of-care hearing aid fittings. Taken together, the questions addressed in this project will provide a comprehensive assessment of the effects of hearing aid processing in realistic listening environments, while considering specific abilities that affect response to signal processing.